In this section, we further use experiments to verify the effectiveness of AMSGrad-EG and AMSGrad-EG-DRD algorithms proposed by us. Also, we show that the one-sided MVI condition is more feasible than standard MVI condition even in a more complicated setting. Here, we use DCGAN [1]} on CIFAR10 dataset. We set our batch size as 100, learning rate as \(1e\) -4 and we compare AMSGrad-EG, AMSGrad-EG-DRD and SGDA by drawing their generated figures after 50, 100, 200 iterations in the following Figure 2. The two algorithms proposed by us again perform better than the non-adaptive SGDA. After training DCGAN on the CIFAR10 dataset with AMSGrad-EG optimizer, we plot the total MVI values \(\langle -V(z_k), z_k-z^*\rangle \) , \(x\) -sided MVI values \(\langle -V_x(z_k), x_k-x^*\rangle \) , and \(y\) -sided MVI values \(\langle -V_y(z_k), y_k-y^*\rangle \)  along the training trajectory as the following Figure REF .
